Collision Free

Collision-free path planning focuses on generating trajectories for robots and autonomous systems that avoid collisions with obstacles and other agents, optimizing for factors like speed, energy efficiency, and smoothness. Current research emphasizes efficient algorithms like A*, Model Predictive Control (MPC), and various sampling-based methods (e.g., RRT*), often enhanced by machine learning techniques such as reinforcement learning and diffusion models to handle complex environments and dynamic obstacles. These advancements are crucial for enabling safe and efficient operation of robots in diverse applications, from warehouse automation and autonomous driving to multi-robot collaboration and aerial navigation.